Biology subjects

Beer, J. C.

Publications and source records attributed to Beer, J. C..

4 recordsLinked to original sources

bioRxiv2023

Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

immunology↗

bioRxiv2020

Preferential recognition and antagonism of SARS-CoV-2 spike glycoprotein binding to 3-O-sulfated heparan sulfate

The COVID-19 pandemic caused by SARS-CoV-2 is in immediate need of an effective antidote. Although the Spike glycoprotein (SgP) of SARS-CoV-2 has been shown to bind to heparins, the structural features of this interaction, the role of a plausible heparan sulfate proteoglycan (HSPG) receptor, and the antagonism of this pathway through small molecules remain unaddressed. Using an in vitro cellular assay, we demonstrate HSPGs modified by the 3-O-sulfotransferase isoform-3, but not isoform-5, preferentially increased SgP-mediated cell-to-cell fusion in comparison to control, unmodified, wild-type HSPGs. Computational studies support preferential recognition of the receptor-binding domain of SgP by 3-O-sulfated HS sequences. Competition with either fondaparinux, a 3-O-sulfated HS-binding oligopeptide, or a synthetic, non-sugar small molecule, blocked SgP-mediated cell-to-cell fusion. Finally, the synthetic, sulfated molecule inhibited fusion of GFP-tagged pseudo SARS-CoV-2 with human 293T cells with sub-micromolar potency. Overall, overexpression of 3-O-sulfated HSPGs contribute to fusion of SARS-CoV-2, which could be effectively antagonized by a synthetic, small molecule.

biochemistry↗

bioRxiv2019

A Method for Harmonizing Longitudinal Multi-scanner Imaging Data

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

neuroscience↗

bioRxiv2019

Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing scanner-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of multivariate pattern analysis (MVPA) has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing scanner effects in mean and variance may not be sufficient for MVPA. This stems from the fact that such methods fail to address how correlations between measurements can vary across scanners. Data from the Alzheimers Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across scanners and that popular harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization.

neuroscience↗